Perceived Challenges in Primary Literature in a Master’s Class: Effects of Experience and Instruction

Primary literature offers rich opportunities to teach students how to “think like a scientist,” but the challenges students face when they attempt to read research articles are not well understood. Here, we present an analysis of what master’s students perceive as the most challenging aspects of engaging with primary literature. We examined 69 pairs of pre- and postcourse responses from students enrolled in a master’s-level course that offered a structured analysis of primary literature. On the basis of these responses, we identified six categories of challenges. Before instruction, “techniques” and “experimental data” were the most frequently identified categories of challenges. The majority of difficulties students perceived in the primary literature corresponded to Bloom’s lower-order cognitive skills. After instruction, “conclusions” were identified as the most difficult aspect of primary literature, and the frequency of challenges that corresponded to higher-order cognitive skills increased significantly among students who reported less experience with primary literature. These changes are consistent with a more competent perception of the primary literature, in which these students increasingly focus on challenges requiring critical thinking. Students’ difficulties identified here can inform the design of instructional approaches aimed to teach students how to critically read scientific papers

Teaching Real Data Interpretation with Models (TRIM): Analysis of Student Dialogue in a Large-Enrollment Cell and Developmental Biology Course

We present our design for a cell biology course to integrate content with scientific practices, specifically data interpretation and model-based reasoning. A 2-yr research project within this course allowed us to understand how students interpret authentic biological data in this setting. Through analysis of written work, we measured the extent to which students' data interpretations were valid and/or generative. By analyzing small-group audio recordings during in-class activities, we demonstrated how students used instructor-provided models to build and refine data interpretations. Often, students used models to broaden the scope of data interpretations, tying conclusions to a biological significance. Coding analysis revealed several strategies and challenges that were common among students in this collaborative setting. Spontaneous argumentation was present in 82% of transcripts, suggesting that data interpretation using models may be a way to elicit this important disciplinary practice. Argumentation dialogue included frequent co-construction of claims backed by evidence from data. Other common strategies included collaborative decoding of data representations and noticing data patterns before making interpretive claims. Focusing on irrelevant data patterns was the most common challenge. Our findings provide evidence to support the feasibility of supporting students' data-interpretation skills within a large lecture course.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]